Robust ambulance base allocation strategy with social media and traffic congestion information

Yuangyai, Chumpol; Nilsang, Suriyaphong; Cheng, Chen-Yang

doi:10.1007/s12652-020-01889-0

Cited by 8 publications

(10 citation statements)

References 44 publications

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“…This model minimizes the number of relocations for a planning horizon while maintaining an acceptable level of service. Another relevant stochastic approach was proposed by Toro-Díaz et al [100]. This approach defines a queuing model within the internal structure of a mathematical model corresponding to a Markov process of continuous-time and finite states.…”

Section: Modeling Issuesmentioning

confidence: 99%

“…For example, Tsai et al [89] use time series smoothing to tackle stochasticity to make better relocation decisions. Other researchers deal with demand stochasticity with hypercube models like in [100,110,114,115,126,128]. Other studies deal with stochasticity in travel times using machine learning [130], as Olave-Rojas and Nickel [131] have previously done.…”

Section: Modeling Issuesmentioning

confidence: 99%

“…Another aspect that needs to be considered is whether the solution method allows several vehicles at the same geographic point. Zarandi et al [32], Toro-Díaz et al [100], Chanta et al [87], Billhardt et al [101], Maleki et al [98], Van Barneveld et al [73], among others, do not include this option in their models.…”

Section: Location Capacitymentioning

confidence: 99%

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Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

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Section: Modeling Issuesmentioning

confidence: 99%

Section: Modeling Issuesmentioning

confidence: 99%

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Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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“…Decisions of ambulance location and allocation that minimizes the number of ambulances is difficult due to the limited number of ambulances to cover all of the demand points, and these are critical for EMS management [20]. While location planning decisions are strategic, redeployment problems are operational, and these problems are solved dynamically in real-time, and emergency medical service managers often need to make spontaneous decisions for allocation and redeployment [21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (19) updates the number of ambulances located at the beginning of periods according to their number and location at the end of the previous period. Constraint (20) determines the number of ambulances in locations at the beginning of each period according to the total number of existing ambulances and additional ambulances assigned in the previous period. Here, ambulances added in the previous periods also work in the following periods.…”

Section: First Stage Constraintsmentioning

confidence: 99%

Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Caglayan

Satoğlu

2021

Mathematics

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Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage, and the total transportation time. Besides, we assumed that a data-driven decision support tool is employed to track casualties and up-to-date hospital capacities, so as to direct the ambulances to the available hospitals. For this purpose, a multi-objective two-stage stochastic programming model was developed. The model was applied to a district in Istanbul city of Turkey, for a major earthquake. Accordingly, the model was developed with a holistic perspective with multiple objectives, periods and locations. The developed multi-objective stochastic programming model was solved using an improved version of the augmented ε-constraint (AUGMECON2) method. Hence, the Pareto optimal solutions set has been obtained and compared with the best solution achieved according to the objective of total transportation time, to see the effect of the ambulance direction decisions based on hospital capacity availability. All of the decisions examined in these comparisons were evaluated in terms of effectiveness and equity. Finally, managerial implication strategies were presented to contribute decision-makers according to the results obtained. Results showed that without implementing a data-driven decision support tool, equity in casualty transportation cannot be achieved among the demand points.

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A multi-verse optimizer algorithm for ambulance repositioning in emergency medical service systems

Golabian

Arkat

Tavakkoli–Moghaddam

et al. 2021

J Ambient Intell Human Comput

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Robust ambulance base allocation strategy with social media and traffic congestion information

Cited by 8 publications

References 44 publications

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

A multi-verse optimizer algorithm for ambulance repositioning in emergency medical service systems

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